Constrained Clustering.
A constrained clustering problem differs from the classical clustering scenario as the user has access to some pre-existing knowledge about the desired partition of the data expressed as instance-level constraints [1]}. Traditional clustering methods, such as the well-known K-means algorithm, have been extended to enforce pairwise constraints ([2]}, [3]}). Several methods also proposed a constrained version of the Gaussian Mixture Models [4]}, [5]}, [6]}. Among them, penalized probabilistic clustering (PPC, [7]}) is the most related to our work as it expresses the pairwise constraints as Bayesian priors over the assignment of data points to clusters, similarly to our model. However, all previous mentioned models shows poor performance and high computational complexity on high-dimensional and large-scale data sets.
